Perennial Pepperweed Mapping

Perennial Pepperweed Mapping

Noxious Weeds Monitoring 2025-2026 LORP Workplan

Published

September 18, 2025

Modified

September 18, 2025

Summary

This data report covers the monitoring of Lepidium latifolium (perennial pepperweed) in the Lower Owens River Project (LORP) area from 2018 to 2025. The data is sourced from the ArcGIS Online Noxious Weeds 2025 feature service and provides updated spatial occurrence as of 2025.

“Perennial pepperweed (Lepidium latifolium), an introduced plant from southeastern Europe and Asia, is invasive throughout the western United States. It can establish in a wide range of environments and is a common problem in flood plains, irrigation structures, pastures, wetlands, riparian areas, roadsides, and residential site. Recent surveys identify perennial pepperweed as a weed problem in nearly all of California, and both the California Department of Food and Agriculture (CDFA) and California Invasive Plant Council (Cal-IPC) list it as a noxious weed of great ecological concern.” - UC IPM

California Invasive Plant Council (Cal-IPC) Rating: High
High – These species have severe ecological impacts on physical processes, plant and animal communities, and vegetation structure. Their reproductive biology and other attributes are conducive to moderate to high rates of dispersal and establishment. Most are widely distributed ecologically.

California Department of Food and Agriculture (CDFA) Rating: -*
* – included in the CCR Section 4500 list of California State Noxious Weeds.

LORP Area Filter Applied:
- Northernmost 2025 point latitude: 36.9747 °N
- Filtered dataset now contains 1647 observations

Data Overview

The dataset includes 1647 total observations across 8 years, with peak survey activity in 2025 (426 records).

Key Findings Summary

Total Observations: 1647 records across all years (2018-2025)
Peak Survey Year: 2025 with 426 observations
Recent Activity: 852 observations in 2022-2025
Historical Baseline: 795 observations in 2018-2021
Data Coverage: 100% of records have complete date information (using observation date with creation date fallback)

Interactive Map Comparison

The following interactive map shows pepperweed populations across all years (2018-2025). Use the layer controls in the top-right corner to toggle between different years. The map includes all 1647 documented observations, with feature creation dates used when observation dates are unavailable.

Data Downloads

📊 Download GeoJSON 🌍 Download KML

Complete dataset with all 1647 pepperweed observations

Study Area Boundaries

North: 36.9747°N
South: 36.5478°N
East: -117.981°W
West: -118.2343°W
Center: 36.7612°N, -118.1077°W

2025 Distribution - plants per site estimated from field observations

The following histogram shows the distribution of abundance values for all pepperweed point locations documented in 2025:

### 2025 Site Distribution
- **Total Sites**: 426 
- **Small Sites (≤ 100 plants)**: 325 sites ( 76.3 %)
- **Large Sites (> 100 plants)**: 99 sites ( 23.2 %)

### Detailed Abundance Distribution
- **1 plants**: 57 sites
- **2 plants**: 135 sites
- **4 plants**: 133 sites
- **5 plants**: 99 sites

### Abundance Range
- **Minimum**: 5 plants
- **Maximum**: 200 plants

The interactive map above provides the primary visualization of pepperweed distribution patterns.

Recent Sites

Data Quality and Limitations

Data Sources

Primary Source: ArcGIS Online Noxious Weeds 2025 Feature Service
Last Updated: August 11, 2025
Coordinate System: WGS84 (EPSG:4326)

Limitations

Data represents documented observations only
Population estimates may vary based on survey timing and conditions
Some areas may be under-sampled due to access constraints
Abundance categories are based on field estimates

Field Documentation

Field Documentation Images

Pepperweed observations with field photos:

Feature ID: 1369 | Date: 2025-04-02 | Abundance: 200 plants

Feature ID: 1370 | Date: 2025-04-02 | Abundance: 200 plants

Feature ID: 1371 | Date: 2025-05-14 | Abundance: 200 plants

Notes: Dense vegetative cluster around low ditch

Feature ID: 1398 | Date: 2025-07-30 | Abundance: 200 plants

Notes: Among the tule next to the water, north of the willow

Feature ID: 1401 | Date: 2025-07-30 | Abundance: 200 plants

Notes: Among tule next to water, south of willow

Feature ID: 1465 | Date: 2025-07-30 | Abundance: 200 plants

Notes: W BAHY + SCAC

Feature ID: 1467 | Date: 2025-07-30 | Abundance: 200 plants

Notes: 10-15m south of rivers edge

Feature ID: 1473 | Date: 2025-07-30 | Abundance: 200 plants

Notes: 8-10m from waters edge; 12-15m long stretch of LELA bordering large TARA

Feature ID: 1475 | Date: 2025-07-30 | Abundance: 200 plants

Notes: 20m long stretch 2-5m from river, dense

Feature ID: 1482 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1485 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W/ ASFA

Feature ID: 1490 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W/ dead BAHY

Feature ID: 1495 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W) ASFA ELTR TYPHA

Feature ID: 1501 | Date: 2025-07-31 | Abundance: 200 plants

Notes: W/ ASFA

Feature ID: 1506 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1507 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1508 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1510 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1515 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1516 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1527 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1529 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1535 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1538 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1545 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1547 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1563 | Date: 2025-07-31 | Abundance: 200 plants

Feature ID: 1583 | Date: 2025-08-04 | Abundance: 200 plants

Notes: Along bank, flowering

Feature ID: 1598 | Date: 2025-08-04 | Abundance: 200 plants

Notes: Several large patches around this point

Feature ID: 1650 | Date: 2025-08-04 | Abundance: 200 plants

Feature ID: 1367 | Date: 2025-04-01 | Abundance: 100 plants

Field documentation images provide visual confirmation of pepperweed sites and help verify site characteristics.

Conclusion

The 2018-2025 data reveals expanding pepperweed distribution within the LORP area. The interactive mapping tool provides land managers with a visualization tool for identifying and prioritizing treatment areas over time.

This report will be updated as new data are added to the live feature service. The data source is available at: ArcGIS REST Service

Data Disclaimer

The data presented in this report are collected for monitoring and management purposes by the Inyo County Water Department. While every effort is made to ensure accuracy, field conditions, survey timing, and observer experience may affect data quality. Users should verify critical information independently before making management decisions.

This analysis is automatically updated when new data is added to the ArcGIS feature service.

Citation

BibTeX citation:

@report{untitled,
  author = {},
  publisher = {Inyo County Water Department},
  title = {Perennial {Pepperweed} {Mapping}},
  date = {},
  url = {https://github.com/inyo-gov/noxious-weeds},
  langid = {en}
}

For attribution, please cite this work as:

“Perennial Pepperweed Mapping.” n.d. Data Report. Inyo County Water Department. https://github.com/inyo-gov/noxious-weeds.

--- title: "Perennial Pepperweed Mapping" subtitle: "Noxious Weeds Monitoring 2025-2026 LORP Workplan" tbl-cap-location: top affiliation: "Inyo County Water Department" date: today date-modified: today citation: type: report container-title: "Data Report" publisher: "Inyo County Water Department" issued: today url: https://github.com/inyo-gov/noxious-weeds google-scholar: true params: current_year: 2025 start_year: 2018 format: html: theme: cosmo toc: true toc-depth: 3 code-fold: true code-tools: true embed-resources: true css: styles.css docx: toc: true toc-depth: 3 number-sections: true reference-doc: custom-reference.docx execute: echo: false warning: false message: false prefer-html: true --- ```{r setup} #| message: false #| warning: false library(sf) library(dplyr) library(tidyr) library(leaflet) library(leaflet.extras2) library(lubridate) library(htmltools) library(knitr) library(DT) library(ggplot2) library(scales) library(httr) ``` # Summary This data report covers the monitoring of **Lepidium latifolium** (perennial pepperweed) in the Lower Owens River Project (LORP) area from `r params$start_year` to `r params$current_year`. The data is sourced from the [ArcGIS Online Noxious Weeds 2025 feature service](https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0) and provides updated spatial occurrence as of `r params$current_year`. > "Perennial pepperweed (Lepidium latifolium), an introduced plant from southeastern Europe and Asia, is invasive throughout the western United States. It can establish in a wide range of environments and is a common problem in flood plains, irrigation structures, pastures, wetlands, riparian areas, roadsides, and residential site. Recent surveys identify perennial pepperweed as a weed problem in nearly all of California, and both the California Department of Food and Agriculture (CDFA) and California Invasive Plant Council (Cal-IPC) list it as a noxious weed of great ecological concern." - [UC IPM](https://ipm.ucanr.edu/home-and-landscape/perennial-pepperweed/#gsc.tab=0) **California Invasive Plant Council (Cal-IPC) Rating: High** High – These species have severe ecological impacts on physical processes, plant and animal communities, and vegetation structure. Their reproductive biology and other attributes are conducive to moderate to high rates of dispersal and establishment. Most are widely distributed ecologically. **California Department of Food and Agriculture (CDFA) Rating: -*** * – included in the CCR Section 4500 list of California State Noxious Weeds. ```{r data-loading} #| message: false #| warning: false # ArcGIS Feature Service URL base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0/query?" # Query parameters for all data query_params <- paste0( "where=1=1", "&outFields=*", "&f=geojson", "&outSR=4326" ) # Construct full query URL query_url <- paste0(base_url, query_params) # Load data from ArcGIS feature service pepper_data <- st_read(query_url, quiet = TRUE) # Process dates - convert Unix timestamps to dates pepper_data <- pepper_data %>% mutate( Date_Observed = as.POSIXct(Date/1000, origin='1970-01-01'), Date_Created = as.POSIXct(CreationDate/1000, origin='1970-01-01'), Year_Observed = year(Date_Observed), Year_Created = year(Date_Created), # Use creation year as fallback when observation year is missing Year_Display = ifelse(is.na(Year_Observed), Year_Created, Year_Observed), Date_Display = as.POSIXct(ifelse(is.na(Date_Observed), as.numeric(Date_Created), as.numeric(Date_Observed)), origin='1970-01-01') ) # Filter to LORP area based on northernmost current year data point pepper_current_year_for_bounds <- pepper_data %>% filter(Year_Display == params$current_year) if(nrow(pepper_current_year_for_bounds) > 0) { # Get the northernmost latitude from current year data northernmost_lat <- max(st_coordinates(pepper_current_year_for_bounds)[,2]) # Filter all data to LORP area (south of northernmost current year point) pepper_data <- pepper_data %>% filter(st_coordinates(.)[,2] <= northernmost_lat) cat("LORP Area Filter Applied:\n") cat("- Northernmost", params$current_year, "point latitude:", round(northernmost_lat, 4), "°N\n") cat("- Filtered dataset now contains", nrow(pepper_data), "observations\n") } else { cat("No", params$current_year, "data found for LORP area filtering\n") } # Include all records (no year filtering) # Separate data by display year (observation year with creation year fallback) pepper_2018 <- pepper_data %>% filter(Year_Display == 2018) pepper_2019 <- pepper_data %>% filter(Year_Display == 2019) pepper_2020 <- pepper_data %>% filter(Year_Display == 2020) pepper_2021 <- pepper_data %>% filter(Year_Display == 2021) pepper_2022 <- pepper_data %>% filter(Year_Display == 2022) pepper_2023 <- pepper_data %>% filter(Year_Display == 2023) pepper_2024 <- pepper_data %>% filter(Year_Display == 2024) pepper_2025 <- pepper_data %>% filter(Year_Display == 2025) # Create recent vs historical comparison pepper_recent <- pepper_data %>% filter(Year_Display >= 2022) pepper_historical <- pepper_data %>% filter(Year_Display >= 2018 & Year_Display <= 2021) # Summary statistics for all years (using display year with fallback) summary_stats <- pepper_data %>% filter(!is.na(Year_Display)) %>% group_by(Year_Display) %>% summarise( Total_Observations = n(), Avg_Abundance = round(mean(Abundance, na.rm = TRUE), 1), Avg_Height = round(mean(Height, na.rm = TRUE), 1), Max_Abundance = max(Abundance, na.rm = TRUE), Records_With_Obs_Date = sum(!is.na(Year_Observed)), Records_With_Creation_Date_Only = sum(is.na(Year_Observed) & !is.na(Year_Created)), .groups = 'drop' ) %>% arrange(Year_Display) ``` ## Data Overview The dataset includes `r nrow(pepper_data)` total observations across `r length(unique(pepper_data$Year_Display[!is.na(pepper_data$Year_Display)]))` years, with peak survey activity in `r names(sort(table(pepper_data$Year_Display, useNA='ifany'), decreasing=TRUE)[1])` (`r max(table(pepper_data$Year_Display, useNA='ifany'))` records). ## Key Findings Summary - **Total Observations**: `r nrow(pepper_data)` records across all years (2018-2025) - **Peak Survey Year**: `r names(sort(table(pepper_data$Year_Display, useNA='ifany'), decreasing=TRUE)[1])` with `r max(table(pepper_data$Year_Display, useNA='ifany'))` observations - **Recent Activity**: `r nrow(pepper_recent)` observations in 2022-2025 - **Historical Baseline**: `r nrow(pepper_historical)` observations in 2018-2021 - **Data Coverage**: `r round((nrow(pepper_data) - sum(is.na(pepper_data$Year_Display))) / nrow(pepper_data) * 100, 1)`% of records have complete date information (using observation date with creation date fallback) # Interactive Map Comparison The following interactive map shows pepperweed populations across all years (`r params$start_year`-`r params$current_year`). Use the layer controls in the top-right corner to toggle between different years. The map includes all `r nrow(pepper_data)` documented observations, with feature creation dates used when observation dates are unavailable. <div class="download-section"> <h3>Data Downloads</h3> <div class="download-links"> <a href="pepperweed_data.geojson">📊 Download GeoJSON</a> <a href="pepperweed_data.kml">🌍 Download KML</a> </div> Complete dataset with all `r nrow(pepper_data)` pepperweed observations </div> ```{r generate-geojson} #| message: false #| warning: false #| echo: false #| results: hide # Generate GeoJSON file for download st_write(pepper_data, "pepperweed_data.geojson", driver = "GeoJSON", delete_dsn = TRUE, quiet = TRUE) # Generate KML file for Google Earth (will be zipped to KMZ) st_write(pepper_data, "pepperweed_data.kml", driver = "KML", delete_dsn = TRUE, quiet = TRUE) # Calculate spatial extent bbox <- st_bbox(pepper_data) extent_info <- list( north = bbox$ymax, south = bbox$ymin, east = bbox$xmax, west = bbox$xmin, center_lat = (bbox$ymax + bbox$ymin) / 2, center_lon = (bbox$xmax + bbox$xmin) / 2 ) ``` <div class="spatial-extent"> <h4>Study Area Boundaries</h4> <ul> <li>North: `r round(extent_info$north, 4)`°N</li> <li>South: `r round(extent_info$south, 4)`°N</li> <li>East: `r round(extent_info$east, 4)`°W</li> <li>West: `r round(extent_info$west, 4)`°W</li> <li>Center: `r round(extent_info$center_lat, 4)`°N, `r round(extent_info$center_lon, 4)`°W</li> </ul> </div> ```{r interactive-map} #| message: false #| warning: false # Create comprehensive map with all years combined_map <- leaflet(pepper_data) %>% # Standard basemaps addProviderTiles(providers$Esri.WorldImagery, group = "World Imagery") %>% addProviderTiles(providers$OpenStreetMap, group = "OpenStreetMap") %>% # Add 2018 markers addCircleMarkers( data = pepper_2018, radius = 5, color = "black", fillColor = "black", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2018 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2018 Data" ) %>% # Add 2019 markers addCircleMarkers( data = pepper_2019, radius = 5, color = "darkred", fillColor = "darkred", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2019 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2019 Data" ) %>% # Add 2020 markers addCircleMarkers( data = pepper_2020, radius = 5, color = "red", fillColor = "red", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2020 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2020 Data" ) %>% # Add 2021 markers addCircleMarkers( data = pepper_2021, radius = 5, color = "yellow", fillColor = "yellow", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2021 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2021 Data" ) %>% # Add 2022 markers addCircleMarkers( data = pepper_2022, radius = 5, color = "orange", fillColor = "orange", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2022 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2022 Data" ) %>% # Add 2023 markers addCircleMarkers( data = pepper_2023, radius = 5, color = "green", fillColor = "green", fillOpacity = 0.7, weight = 2, popup = ~paste0( "2023 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2023 Data" ) %>% # Add 2024 markers addCircleMarkers( data = pepper_2024, radius = 5, color = "blue", fillColor = "blue", fillOpacity = 0.8, weight = 3, popup = ~paste0( "2024 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2024 Data" ) %>% # Add 2025 markers addCircleMarkers( data = pepper_2025, radius = 5, color = "purple", fillColor = "purple", fillOpacity = 0.9, weight = 3, popup = ~paste0( "2025 Data ", "Date: ", format(Date_Display, "%Y-%m-%d"), " ", "Date Source: ", ifelse(is.na(Year_Observed), "Creation Date", "Observation Date"), " ", "Species: ", Species, " ", "Abundance: ", Abundance, " ", "Height: ", ifelse(is.na(Height), "Unknown", paste(Height, "m")), " ", "Notes: ", ifelse(is.na(Notes), "None", Notes) ), group = "2025 Data" ) %>% addLayersControl( baseGroups = c("World Imagery", "OpenStreetMap"), overlayGroups = c("2018 Data", "2019 Data", "2020 Data", "2021 Data", "2022 Data", "2023 Data", "2024 Data", "2025 Data"), options = layersControlOptions(collapsed = FALSE) ) %>% addLegend( position = "bottomright", colors = c("black", "darkred", "red", "yellow", "orange", "green", "blue", "purple"), labels = c("2018", "2019", "2020", "2021", "2022", "2023", "2024", "2025"), title = "Pepperweed Distribution by Year" ) combined_map ``` ## `r params$current_year` Distribution - plants per site estimated from field observations The following histogram shows the distribution of abundance values for all pepperweed point locations documented in `r params$current_year`: ```{r abundance-histogram} #| message: false #| warning: false # Filter for current year data only pepper_current_year <- pepper_data %>% filter(Year_Display == params$current_year) # Create abundance histogram if(nrow(pepper_current_year) > 0) { # Create abundance bins (preserve all bins, exclude empty ones) abundance_data <- pepper_current_year %>% filter(!is.na(Abundance)) %>% mutate( Abundance_Bin = case_when( Abundance <= 10 ~ "1-10", Abundance <= 25 ~ "11-25", Abundance <= 50 ~ "26-50", Abundance <= 100 ~ "51-100", TRUE ~ ">100" ), Abundance_Bin = factor(Abundance_Bin, levels = c("1-10", "11-25", "26-50", "51-100", ">100"), ordered = TRUE) ) # Only include bins that have data abundance_counts <- abundance_data %>% count(Abundance_Bin) %>% filter(n > 0) # Create bar chart with only populated bins hist_plot <- ggplot(abundance_counts, aes(x = Abundance_Bin, y = n)) + geom_bar(stat = "identity", fill = "#2E86AB", color = "white", alpha = 0.8, width = 0.7) + labs( title = paste("Distribution of Pepperweed Abundance in", params$current_year), subtitle = paste("Total sites:", nrow(pepper_current_year)), x = "Abundance Range (plants)", y = "Number of Sites" ) + theme_minimal() + theme( plot.title = element_text(size = 16, face = "bold", color = "#2E86AB"), plot.subtitle = element_text(size = 14, color = "#666666"), axis.title = element_text(size = 12), axis.text.x = element_text(angle = 45, hjust = 1), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank() ) + scale_y_continuous(breaks = scales::pretty_breaks(n = 8)) print(hist_plot) # Management categories small_pops <- sum(pepper_current_year$Abundance <= 100, na.rm = TRUE) large_pops <- sum(pepper_current_year$Abundance > 100, na.rm = TRUE) cat("###", params$current_year, "Site Distribution\n") cat("- **Total Sites**:", nrow(pepper_current_year), "\n") cat("- **Small Sites (≤ 100 plants)**:", small_pops, "sites (", round(small_pops/nrow(pepper_current_year)*100, 1), "%)\n") cat("- **Large Sites (> 100 plants)**:", large_pops, "sites (", round(large_pops/nrow(pepper_current_year)*100, 1), "%)\n") # Detailed bin counts cat("\n### Detailed Abundance Distribution\n") for(i in 1:nrow(abundance_counts)) { bin_name <- abundance_counts$Abundance_Bin[i] bin_count <- abundance_counts$n[i] cat("- **", bin_name, " plants**: ", bin_count, " sites\n", sep = "") } # Range information cat("\n### Abundance Range\n") cat("- **Minimum**:", min(pepper_current_year$Abundance, na.rm = TRUE), "plants\n") cat("- **Maximum**:", max(pepper_current_year$Abundance, na.rm = TRUE), "plants\n") } else { cat("No", params$current_year, "data available for abundance analysis.\n") } ``` The interactive map above provides the primary visualization of pepperweed distribution patterns. ### Recent Sites ```{r new-sites} #| message: false #| warning: false # Show all recent sites (2024-2025) - prioritize small sites for early intervention recent_sites <- pepper_data %>% filter(Year_Display >= 2024) %>% select(Date_Display, Abundance, Height, Notes, Year_Display) %>% # Sort by abundance (smallest first) to prioritize satellite sites arrange(Abundance, desc(Year_Display)) if(nrow(recent_sites) > 0) { datatable(recent_sites, caption = "Recent Pepperweed Sites (2024-2025) - Prioritize Small Sites for Early Intervention", options = list(pageLength = 15, scrollX = TRUE)) } else { cat("No recent sites found.") } ``` # Data Quality and Limitations ## Data Sources - **Primary Source**: [ArcGIS Online Noxious Weeds 2025 Feature Service](https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0) - **Last Updated**: `r format(as.POSIXct(max(pepper_data$CreationDate, na.rm = TRUE)/1000, origin='1970-01-01'), "%B %d, %Y")` - **Coordinate System**: WGS84 (EPSG:4326) ## Limitations - Data represents documented observations only - Population estimates may vary based on survey timing and conditions - Some areas may be under-sampled due to access constraints - Abundance categories are based on field estimates # Field Documentation ```{r field-images} #| message: false #| warning: false #| results: 'asis' # Function to check if a feature has attachments check_feature_attachments <- function(objectid) { base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0" attachments_url <- paste0(base_url, "/", objectid, "/attachments?f=json") response <- GET(attachments_url) if(response$status_code == 200) { attachments_data <- content(response, 'parsed') return(length(attachments_data$attachmentInfos) > 0) } return(FALSE) } # Function to get attachments for a feature get_feature_attachments <- function(objectid) { base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0" attachments_url <- paste0(base_url, "/", objectid, "/attachments?f=json") response <- GET(attachments_url) if(response$status_code == 200) { attachments_data <- content(response, 'parsed') return(attachments_data$attachmentInfos) } else { return(NULL) } } # Function to display an image from attachment display_attachment_image <- function(objectid, attachment_id, width = 500) { base_url <- "https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0" image_url <- paste0(base_url, "/", objectid, "/attachments/", attachment_id) # Create HTML for image display html_content <- paste0( '<div style="display: inline-block; margin: 15px; text-align: center;">', '<img src="', image_url, '" style="max-width: ', width, 'px; height: auto; border: 2px solid #ddd; border-radius: 8px; box-shadow: 0 4px 8px rgba(0,0,0,0.15);">', '</div>' ) # Use cat to output HTML directly cat(html_content) } # Find features with attachments (check more features for up to 40 photos) cat("## Field Documentation Images\n") cat("Pepperweed observations with field photos:\n\n") # Check which features have attachments - look at more features to get up to 40 photos features_to_check <- pepper_data %>% filter(Year_Display >= params$current_year - 2) %>% # Include last 2 years arrange(desc(Year_Display), desc(Abundance)) %>% head(100) # Check more features to find ones with attachments features_with_attachments <- c() for(i in 1:nrow(features_to_check)) { objectid <- features_to_check$OBJECTID[i] if(check_feature_attachments(objectid)) { features_with_attachments <- c(features_with_attachments, objectid) } } # Display images for features that have attachments (up to 40) images_displayed <- 0 if(length(features_with_attachments) > 0) { for(objectid in features_with_attachments[1:min(40, length(features_with_attachments))]) { # Get feature data feature <- pepper_data[pepper_data$OBJECTID == objectid,] # Get attachments attachments <- get_feature_attachments(objectid) if(!is.null(attachments) && length(attachments) > 0) { # Get the first image attachment first_attachment <- attachments[[1]] # Display image first display_attachment_image(objectid, first_attachment$id) # Display metadata below the image cat("<div style='text-align: center; margin-bottom: 30px;'>") cat("Feature ID:", objectid, "| Date:", format(feature$Date_Display, "%Y-%m-%d"), "| Abundance:", feature$Abundance, "plants") # Include Notes if available if(!is.na(feature$Notes) && feature$Notes != "") { cat("Notes:", feature$Notes, "") } cat("</div>") images_displayed <- images_displayed + 1 } } } if(images_displayed == 0) { cat("No field documentation images available for recent observations.\n") } else { cat("\n*Field documentation images provide visual confirmation of pepperweed sites and help verify site characteristics.*\n") } ``` # Conclusion The `r params$start_year`-`r params$current_year` data reveals expanding pepperweed distribution within the LORP area. The interactive mapping tool provides land managers with a visualization tool for identifying and prioritizing treatment areas over time. This report will be updated as new data are added to the live feature service. The data source is available at: [ArcGIS REST Service](https://services.arcgis.com/0jRlQ17Qmni5zEMr/arcgis/rest/services/Noxious_Weeds_2025_view/FeatureServer/0) ## Data Disclaimer The data presented in this report are collected for monitoring and management purposes by the Inyo County Water Department. While every effort is made to ensure accuracy, field conditions, survey timing, and observer experience may affect data quality. Users should verify critical information independently before making management decisions. --- **Inyo County Water Department** © 2025 Inyo County, California. All rights reserved. *This analysis is automatically updated when new data is added to the ArcGIS feature service.*